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Record W1998734719 · doi:10.1017/s1355770x02000062

The impacts of economic reform on the efficiency of silviculture: a non-parametric approach

2002· article· en· W1998734719 on OpenAlexaff
Yaoqi Zhang

Bibliographic record

VenueEnvironment and Development Economics · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsData envelopment analysisEconomic efficiencyEconomicsEconomic reformChinaPanel dataProductive efficiencyNatural resource economicsMacroeconomicsEconometricsPolitical scienceMarket economyProduction (economics)Mathematics

Abstract

fetched live from OpenAlex

Institutions and organizations are regarded as being important in determining the efficiency of economic agents and public units. This study first reviews the economic reforms in silvicultural activities in China's state-owned forestry bureaux, then empirically examines the impact of economic reforms. Panel data from 40 forestry bureaux in Heilongjiang Province, and two different economic regimes: from the pre-reform and economic transition periods, are analyzed by Data Envelopment Analysis (DEA). The technical efficiency has been decomposed into pure technical efficiency and scale efficiency and then examined. Our results show that the economic reforms have increased efficiency on average by about 25 per cent. Moreover, the study qualitatively analyses the sources of improvement and argues that the efficiency gain is a result of reductions in labour shirking and administration costs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0000.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.176
Teacher spread0.165 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations21
Published2002
Admission routes1
Has abstractyes

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